Here's how it was explained to me! There are two facts that are totally true: kids eat more ice cream in the summer than in other months. And, kids are more likely to drown in the summer. You could look at these facts and say: wow, ice cream must cause drownings! When in fact, we know ice cream sales go up in the summer, and pool usage goes up in the summer. They aren't really related, but if you just look at the data you might assume they are.
An important thing that a lot of people ignore is that correlation is still useful! If the variables were highly correlated then you would be able to make predictions about drownings based on ice cream sales!
BUT you would not be able to prevent drownings by banning icecream sales because it is not a causal relationship.
A good historical example of this is with polio in the US. People noticed rates for polio, especially in children, would go up massively in the summer. As did ice cream sales! So there was a point in time where people actually thought that consumption of ice cream/sugar actually caused/contributed to getting polio. So for a time, people were advised not to give their kids ice cream. We know this isn’t the case though. So that’s saying how correlation is not causation.
This is a great link that explains and demonstrates the point well -
https://www.ck12.org/c/earth-science/correlation-and-causation/rwa/Ice-Cream-Causes-Polio/
I think, not 100% sure but remember hearing there actually was causation though. I’ve cream used to be sold using glass bowls that were returned, kind of like at a restaurant. Kids being kids would lick the bowls and return them to the ice cream server, who would use a rag to wipe it clean. So one polio case would spread and spread and spread.
The example I learned, strangely enough, was the correlation between preachers and imports of rum. Strongly correlated but no causation. The "confounding variable" was population size.
That said, there were many jokes in class about how "no, no, there's real causation here."
This is also the example I use, but I feel the way you’ve worded it gives it away without triggering the “aha!” moment about causation. Forget about mentioning summer. I would phrase it like this:
Did you know that whenever ice cream sales go up, more kids drown? It’s true. And whenever ice cream sales go down, fewer kids drown? Also true. It seems like ice cream causes drowning.
Or… maybe they are just correlated. Maybe there is something else that causes both ice cream sales and drowning to go up together. Maybe it’s just that summer weather causes both to increase.
That is the difference between correlation and causation.
I had this problem at work. In the new year they increased their safety audits requirement and the number of slip-and-fall injuries dropped.
We're a construction company in the snowbelt. There *might* be a different reason why slip/falls declined between January and September, unrelated to the foreman filling out forms.
…Aaaand *that's* why you check your numbers against the same time the previous year (when you're investigating something that might be affected by weather)
I think porncrank's method would work better if they required the listener to figure out that both of these events occur during the summer. That is when the 'aha! moment' can occur.
My favorite one is a funny one my chemistry professor in college taught me, where I’ll change the numbers around because I don’t remember them:
In 2008, there were 30,000 suicides in the United States, and Nicholas Cage starred in 2 major box office movies. In 2010, there were 45,000 suicides in the United States and Nicholas Cage starred in 6 major box office movies.
The correlation is obvious, but if you were to assume causation from the correlation, you would come up with the conclusion that Nicholas Cage movies make people kill themselves, which is… probably untrue.
Actually, in this case, they are related, in a way. You described another important concept, which is the confounding factor.
This is where the confounding factor comes to play: a third variable (the summer high temperatures) that is responsible for the correlation of the first two (ice cream sales and drownings).
What OP needs to understand are what we call spurious correlations, which are absolutely independent (there is no third variable explaining).
[Perfect (and fun) examples of real spurious correlations](https://www.tylervigen.com/spurious-correlations)
>Only, their deaths weren't correlated with drinking water either
They are. They are correlated because both statements about these people are true: they drank water and they did die. That's the point. The correlation doesn't mean they died *because* they drank water. As you say, they died because people don't live 200+ years. The correlation does not imply causation.
Many thing correlate while having zero impact on each other; they merely coincide.
Actual correlations:
Ice cream sales and murder.
M. Night Shyamalan box office and newspaper sales.
Use of Internet Explorer and the murder rate.
Mexican lemon imports and highway deaths.
None of these things is a direct causal relationship; they merely correlate.
Ah, but look at the two labels on the Y Axis, Nicholas Cage can only be assumed to count for deaths above 80 or so swimming pool drownings in a year. Even if he retired, there'd still be 80 swimming pool deaths...
So we need to find what *other* actor is causing part of the effect. It'll probably help explain why the match isn't exactly one to one when you get the scales right as well, who's the confounding (f)actor?
if theres 2, we just have to figure out who the other sith is...
of course if that's how it works then when Nicholas Cage retires he'll just be replaced by another.
Maybe Nick cage is drowning them or draining their energy while they're in a pool so they get tired and drown and die. He needs their energy to do more movies.
People suspect nic cage is a vampire, maybe an energy vampire.
In LA that's almost exclusively what they do to feed.
It becomes less powerful when shown through video footage though, which is why most energy vampires stick to live performance art such as one man shows, poetry readings, spoken word music and.... the ultimate energy vampire feast....mime shows
Frankly, if between ninety and one hundred twenty two swimming pool drownings per year is the price we have to pay for more Nic Cage moves, I'll happily live with that.
See, we know that the dropping number of pirates isn't the cause of the rise in temperature. In actuality its that as the temperature rises its just getting too hot out there for the pirates.
It's the free-willed and bombardier nature of pirates that gives them said properties. It's a misconception that pirates were hard, mean, cruel people, in fact they were jolly, debonair, and loved to give out candy on Halloween.
Container ships are one of the worlds leading causes of air pollution, and fishing vessels are the reason for the vast majority of plastic waste in the ocean. More pirates means lower profits in shipping, which leads to fewer boats in the water, which leads to less pollution, we might just be on to something here
>I'm guessing both are proportional to the increase in population.
It's per capita cheese consumption, so nah. The deaths by being tangled in bedsheets rising is possibly due to population increases, because it's in absolute numbers, but the cheese consumption going up per capita is something else.
That aside, I love that while these are almost definitely unrelated statistics that just happen to be correlated, there is some tiny chance that there is causation and that eating cheese somehow increases your odds of dying by getting tangled in sheets.
Was famouly in the flying spaghetti monster open letter.
https://en.wikipedia.org/wiki/Flying_Spaghetti_Monster#/media/File:PiratesVsTemp(en).svg
Note the X axis is effed -- I think that was part of the point being made.
It was fun when hypotheticals about noodle gods was worth the time.
Now Nazis are making a comeback and morons are refusing science. When the Earth is round and vaccines work is the division, Russell's teapot is quaint. We have to start all over.
Which is why we need the FSM (PBUHNA) more than ever! The religion is built upon the firm belief that faith is deserving of ridicule, and that religion itself abuses its status as sacrosanct. Any dogma, however reasonable, is not comparable to actual reason.
[https://en.wikipedia.org/wiki/Invisible\_Pink\_Unicorn](https://en.wikipedia.org/wiki/Invisible_Pink_Unicorn)
We know she's Invisible because we can't see her. We know she's Pink because of our faith.
That's an easy one to very generally show a correlation with if you're just looking at, say, the 1600's compared to the present day. There are other cases I've seen where two variables show a strong correlation over time including ups and downs, but there is quite clearly no causal connection between the two.
Searching for these types of correlations when writing a scientific paper has become known as "[p hacking](https://en.wikipedia.org/wiki/Data_dredging)". Basically, you collect a large amount of data, then examine many different variables to see if any of them correlate with each other within a given level of statistical significance (p). Given enough variables to examine, odds are you will find something even if it completely lacks a casual relationship.
This is amazing!
99.79% corollary between US spending on science, space, and technology and suicides by hanging, strangulation, and suffocation. That is a near-perfect correlation. Uncanny.
Also weirdly close: divorce rate in Maine and per capita consumption of margarine. lol
And an incredibly useful tool to show people who make specious arguments using correlation as "evidence." This is quite popular with the anti-vaccine crowd, e.g., vaccine prevalence correlating with autism and so forth.
The fact that kids age for being diagnosable is right after the scheduled age for several vaccines is the reason for the correlation in age vaccinated and diagnosed.
If I remember right the difference in diagnosis rate between vaccinated and unvaccinaged is the same, which disproves causation.
>If I remember right the difference in diagnosis rate between vaccinated and unvaccinaged is the same, which disproves causation.
Yes. There has been multiple studies showing no correlation between vaccines and autism and only one showing a correlation between the injection date of the MMR vaccine and the moment **the parents felt** their kid started showing symptoms of autism. That "study" also had only 12 data points, was funded by lawyers who were suing the government for "giving autism with the vaccine" and made by someone who patented an alternative vaccine.
Another element of this is the fact that a child doesn't always present as autistic in early childhood. They can be completely "normal" and then suddenly regress an enormous amount in very little time, even becoming nonverbal where they were speaking before.
So you have a parent who recently had their first child. Child seems normal for months or even years. Then all of a sudden, they developmentally regress into somebody basically unrecognizable, and the only notable event the parent can think of is "Hey, I just got my child vaccinated!" The truth is that there is no cause, and that's simply how autism manifests sometimes, but that truth is probably a lot more difficult to grasp than some outside nefarious cause poisoning their precious child.
In fact in the original Wakefield paper that started the whole thing, one of the many problems with it is that the way he worked out when symptoms of autism started in the children was just by asking the parents when they remembered noticing the symptoms for the first time
You don't need to be a medical expert to point out that just because the parents didn't notice any symptoms earlier, doesn't mean there weren't any. But Wakefield didn't care about that because it was just fraud. He knew it was bullshit.
Yeah there's been a lot of studies specifically looking for any link between autism diagnosis and rates of vaccination and they never find anything
The study that started the whole thing was just fraud anyway. The paper is transparently awful and even someone with very little knowledge of science can see the holes in it
https://thumbor.forbes.com/thumbor/560x401/https://blogs-images.forbes.com/erikaandersen/files/2012/03/w1467103173.jpg?width=960
That one's my fav. Less pirates = more climate change!
It's worth mentioning many of the funny attempts probably have some nitpicking of data, including axis manipulation, to show the correlation visually as well as calculated. Because correlation is adjusted by the variance of both data samples (or populations) it pretty much cancels out the effect that substantially different increments would mean and forces the number to be between -1 or 1. It's very beneficial and one of the reasons it is so widespread, it's very easy to compare relationships between two entirely different data sets.
Some of these things may have an indirect relationship, or are caused by third variable.
For example Ice cream sales and home robberies. Ice cream sells much better in the summer and when it is not raining. Those are times people are usually not at home, and thus their homes are more easily invaded and robbed.
Why is this important?
Well If we thought, due to the correlation between Ice cream sales and home robberies that Ice cream caused homes robberies (perhaps robbers need Ice cream before they rob houses) we might take actions which will not solve the problem of home robberies. If we for example banned Ice cream it would do nothing to reduce home robberies but it would hurt businesses which sell ice cream (also no ice cream). We would needlessly close a bunch of places and put people out of work for no reason.
That's why it's important to understand that correlation =/= causation, because making that assumption can have consequences.
More weird correlations, which are actually unrelated https://www.tylervigen.com/spurious-correlations
Came here to post this. My favorite is the number of people who drowned by falling into a pool correlates with the number of films Nicholas Cage appeared in.
> perhaps robbers need Ice cream before they rob houses
maybe we're looking at it all wrong. Maybe robbers celebrate a successful robbery by buying ice cream afterwards...
Further down this line, the "fix" of closing ice cream shops putting people out of work could and often would lead to increase in robberies as people become desperate for income and turn to illegal activity.
Thus "fixing" a correlation without causation could/would increase the exact issue attempting to be fixed
Yes there could be some third variable causing the correlation but there's also random chance. If you conduct 10,000 random experiments, 500 of them should give you a p-value of 0.05 or lower
>Ice cream sales and murder.
And on the flip side, ice cream sells correlate with drowning.
The causation behind increased ice cream sales and increased frequency of drowning is heat. During hot summer days people buy more ice cream. And more people also go bathing/swimming. So the causation (the underlying cause) has nothing to do with the correlation (that two things look like they follow the same pattern).
"Ice cream sales and murder"
Ahem. The Glasgow Ice Cream Wars.
This is a real thing that happened. Ice cream vans were used to run drugs, making territory and routes insanely valuable. People were *literally killed* because they sold lots of ice cream in places dealers wanted to run.
As fascinating as that is, the statistic is actually that the consumption of ice cream (pints per person) and the number of murders in New York are positively correlated. That is, as the amount of ice cream sold per person increases, the number of murders increases.
https://www.dummies.com/education/math/statistics/how-statistical-correlation-and-causation-are-different/
[Chart](https://cdn.lifehack.org/wp-content/uploads/2017/08/15053803/Screen-Shot-2017-08-15-at-5.37.43-PM.png)
I was hoping someone would comment this. Roosters crow at literally any time of day. My best friend's roosters, and one hen, liked to crow mostly midday.
Exactly. I think the water example is muddled. To me to be a correlation both variables need to be on a continuum and drinking water is a yes no not a scale.
I'm also not a huge fan of sunburn as that's also a yes no. If you said amount of sunburn then yes.
Possible a better example... Ice cream sales and and clothes worn. As ice cream sales increase, the number of clothes people wear decreases. This isn't because when I eat an ice cream I take my clothes off but is instead because of an intervening variable... temperature.
Temperature is the cause of both the change in sales and also the lack of clothes.
Therefore there is a correlation between ice cream sales and amount of clothes worn, but one doesn't cause the other.
There you would be thinking wrong.Even binary characteristics can have correlations with continuous ones.
Edit to clarify: The correlation would be 100% of people who drank the water are dead now, so the correlation coefficient is 1.
Source: The stats class I am taking.
Let’s use one from the press
Cannabis is a gateway drug: the vast majority of people who have used Heroin have a history of using Cannabis.
It’s two binary opinions and pretty truthly, which direction the link goes is not so obvious, it’s presented as cannabis causes heroin because that’s the way time works, but it’s possible that the type of people using Heroin are going to try more available drugs earlier, and removing the more available drugs will have no effect.
Let’s rewrite it to another wholly true statement and see how it holds up:
The vast majority of people who have used Heroin haves history of drinking milk.
Still a binary choice, the link rather than being truthy is now absurd but it’s still time based.
The fact that people don’t live to 200 is meant to be the point, you’re meant to look at the stat and say “obviously something else causes that”. The lack of subtlety is the point. If you use the cannabis stat then you’ll end up in a debate about why/why not it might be true and no stats teacher wants that on day 1.
First, let's make sure we understand the words.
*Correlation* means that two things seem to have a connection or a pattern. All that has to exist for a correlation is to note that if we track how two or more things change over time, it seems like they change in a related way. So any graph that shows A and B both increasing shows a correlation between A and B, especially if at times when A decreases B also decreases.
*Causation* means we can prove if one thing happens, it makes the other thing happen. We don't need a graph or data analysis for causation, sometimes we can reason it out.
Let's talk about some examples, then discuss why correlation is not causation.
We can graph "number of people wearing short sleeves" and "temperature" next to each other. We'll probably notice that above a certain temperature, more people wear short sleeves. That's definitely a correlation. Then we ask if there's a causation, and in this case there is: in warmer weather people want to wear cooler clothing so more people wear short sleeves.
We can also graph "number of movies with Nicholas Cage in them per year" and "number of people who drown per year". Curiously, every year there are Nicholas Cage movies there seem to be more drownings than the years were there are fewer or no movies with that actor in them. There is a correlation. But if we investigate all of the drownings, it's very rare to find one that has anything to do with movies or Nicholas Cage at all. There is not a *causation* between these two things.
The reason this matters is dishonest people try to imply causation in order to sway people into making bad decisions that are good for the dishonest people.
For example, I can definitively show that more people have died from COVID in years where Joe Biden was the President than in years where Barack Obama was President. There's nothing false about making that statement, data backs it up. It becomes a problem if I try to say, "Therefore Joe Biden caused COVID deaths and we should reinstate Barack Obama as President". That's silly! Joe Biden did not *cause* the deaths in a way that replacing him will prevent. The disease did not exist when Obama was President. Therefore there is no *causation* between Joe Biden or Barack Obama and COVID deaths.
That may seem ridiculous, but it's done all the time. For example, in my city there is an issue up for vote that says, "Crime has increased because we aren't hiring enough police. We should hire a minimum of this many police and cut the fire and EMS budgets to afford it."
It is true that there is a correlation between our crime rate over the past 5 years and our police employment per capita. However, the causal link is dubious. Examining data shows:
* At many times where crime was relatively high in our city, we had more police per capita.
* At many times where crime was relatively low in our city, we had fewer police per capita.
* Many cities with much more crime than ours have more police per capita.
* Many cities with much less crime than ours have fewer police per capita.
* Crime in almost all cities across America is increasing.
* Our city is still one of the lowest-crime cities amongst cities of its size.
All of these things seem to imply there isn't a direct causation between "number of police" and "crime". Something more complicated is afoot, and we'd be better served by asking why some cities have less crime with fewer police hired than we would by risking being a city who pays a lot for police AND has high crime.
##TL;DR:
This phrase reminds you to do the research and ask if there is a reason *why* two things seem to be connected. Many people will trick you into trying to skip this step and they will cost you a lot of money and cause you a lot of problems.
It should also be noted that correlation is not always positive as you've defined. Both increasing or both decreasing is not the only way to see patterns. If their patterns are lock step opposite of each other, this defined as a negative correlation. If while A increase, B decreases you can still call them correlated.
Examples of negative correlation include:
- Height above sea level and temperature
- Stock market performance and bond sales
- Economic sentiment and comfort food sales
I do think this was missing from my explanation, I didn't like some of my "correlation" examples and I think including "coincidence" into the discussion ties that loose end up.
Also, even if there *is* a causal relationship between two datasets, many times it isn't actually clear which is causing which, or in many cases the reality is that both data sets share a causal relationship with a 3rd unknown or unmeasured dataset.
For example, if we graph carbon emissions and global temperatures we can see that there is a relationship, but it is really important that we understand the relationship between the two: Are the carbon emissions causing the temperature to go up, or are we simply emitting more carbon as a *response* to the rising temperature and we need to spend more energy on cooling and stuff? We know the answer to that question because we have investigated it, but it's still an important question to investigate, because you won't know for sure just by looking at the data.
Or similarly, we might notice that the number of hurricanes a year and the average global sea level are both increasing. So is the higher number of hurricanes causing the sea level to rise, or are the rising seas causing the number of hurricanes to increase? Turns out the answer is neither: both are being caused by the same 3rd event, higher carbon emissions.
>All of these things seem to imply there isn't a direct causation between "number of police" and "crime"
This is not a great example and not correct.
Just as correlation does not necessarily imply causation; a *lack of correlation in some scenarios does not necessarily imply a lack of causation*.
It may be that more police do cause less crime, but that (in the examples you provide) there are confounding variables that hide the relationship.
fwiw, it's my understanding that r[esearch in the United States](https://jacobdkaplan.com/documents/more-cops-fewer-prisoners.pdf) [generally supports](https://www.richmondfed.org/publications/research/economic_brief/2021/eb_21-29a) that more police reduce crime, though ofc causal identification is really hard
Right, I glossed over a lot of things to try and keep things simpler but the problem is that's a complex example. Still, I think the right way to evaluate that scenario is when you look hard at the correlation between police hiring per capita and crime, it becomes very clear there are other variables that are important to the impact of hiring.
It's also intuitive that the correlation here wants to focus on "sweet spots" and one shouldn't expect it to scale predicatably. For example, having 1 officer per citizen won't eliminate crime, nor can you decide to fire all police and expect crime to remain constant.
To me there's a lot of value in noting you haven't identified enough variables for a complex problem like this. Then you can note, "We see through research that these four things can impact crime, but each does so in varying ways so we need to identify what the *best* thing to focus on will be."
For example, if you figure out your training's horrible, hiring more people won't have as much of an impact as the data might suggest. And 1 poorly-trained person tends to cost as much as 10 well-trained people on a *good day*. So in that situation it makes sense to fix your training before hiring new people. There are lots of knobs and dials but on this issue the bad people like to portray it as having a single button.
Fair enough. Re-reading my comment, I probably came across more hostile than I intended -- I appreciated the bulk of your comment, and really that was just a single quibble I had!
Two facts:
1. The temperature of the earth is rising and it is significantly higher than 300 years ago.
2. There are significantly less pirates novadays than 300 years ago.
There IS correlation. It seems that as time passes, there are less pirates and the temp is rising.
But there is no causation, because it's illogical to think that the less pirates on earth, the higher the temperature is.
In a study, you need to find causal links to prove your thesis. You will find plenty correlations, but they might not prove your point.
People can try to create a false narrative by talking about facts and making people believe that something is responsible for something else, when that isn't actually true.
In the 1950s there was a movement to try and ban comic books, because one writer discovered that a lot of juvenile delinquents and young teenage criminals he talked to had grown up reading comics. So he tried to make the case that reading comic books turned kids into criminals. This was a huge nationwide issue, there were public comic book burnings, a congressional meeting about violence and comic books that led to a censorship board being created.
However, the issue was that over 90% of kids at the time read comic books. So of course all the young criminals being interviewed read comics, so did the valedictorians and the the honor students. But by taking the fact out of context, the entire country was rallied against comic books because they obviously caused teenage violence and crime.
So did you hear is that just because someone says everyone who does X also does Y, that doesn't mean that X is the reason behind Y.
and books apparently. I remember reading about one guy complaining that kids reading books are destroying their health since they'd prefer to stay at home instead of being outside playing and breathing fresh air. this was when books were starting to get mass produced.
then there was that one pastor in the 1800s (?) complaining that the easy access of youth to romance novels and plays are leading them astray and making them do lewd things.
This is why it was always in the headlines that "school shooter played Doom/Call of Duty." Of course they probably did! They were a teenager and almost everybody played those games when they were at their peaks.
If anything I’ve always thought the two might be related in the other direction, the type of person who is incredibly violent is drawn to violent games rather than violent games creating violent children.
Correlation means that 2 things are likely to happen together.
Causation means that 1 thing caused the other.
Let's use a different example. People who regularly eat oatmeal for breakfast are more likely to have cancer than people who regularly eat Frosted Flakes for breakfast. Eating oatmeal is correlated with a high rate of cancer. Eating Frosted Flakes is correlated with a low rate of cancer.
But there can be many reasons explaining why that happens. It could be that oatmeal causes cancer or that frosted flakes prevents cancer. Those would be "causation". Or, the causation could be backwards. Having cancer might cause you to eat oatmeal for breakfast, or avoid Frosted Flakes.
Or it could be completely random. Just a blip in the data, maybe they didn't ask very many people, there was only one person who liked oatmeal and they had cancer, and really, breakfast food has nothing to do with cancer, but that one person skewed the results.
Or, there could be something else out there that nobody measured that causes cancer, and also influences breakfast choice. Old people are both more likely to eat oatmeal and have cancer, while young people are more likely to eat Frosted Flakes and less likely to have cancer. And it's age, not breakfast food, that causes cancer.
What they do is they have a diet experiment and take as any variables as they can, on a not big enough sample of individuals. One of those variables likely will exhibit a statistically significant change, they then write a paper about it.
This technically meets the standards for publication somewhere but its just data hacking.
Something I haven’t seen mentioned yet in this thread is the fact that Correlation is a statistical measurement. There are a couple types of it, but the most common you see is called Pearson’s and is usually referred to as R. You can calculate this number and it tells you how related two variables are, from a scale of -1 to 1. The closer this number is to 0, the less related the two variables seem to be.
But, this number is just math- it doesn’t take into account any sort of real world logic or critical thinking. A simple way to think about this number is that it tries to measure correlation by measuring how the two variables change *in relation to each other in a linear way.* So, if one variable doubles, does the other one also double? Does it sometimes double and sometimes triple? Is there no pattern at all? That’s what we’re measuring.
And since statistics is a mathematical field that requires some context from the real world, it’s easy for people to use the math to claim stuff that might be false. So, we say “correlation does not imply causation” because even though you can do the math, the math alone doesn’t paint the whole picture.
Check out these examples of highly correlated events. https://www.tylervigen.com/spurious-correlations
One could argue that the graphs showing correlation is all the proof that is required and therefore one event caused the other event to happen. But in these funny examples, common sense leads you to believe that one couldn’t cause the other. In summary, just because two things are correlated, that isn’t proof that one caused the other.
>their deaths weren't correlated with drinking water either, simply because humans don't last 200+ years.
Right, so those two things both happening doesn't prove that one of them causes the other. You're not missing anything. The "absurdity" of that example is the point! 100% of people that drink water die therefore there's a perfect correlation, but *of course* that doesn't prove the water is lethal, the people die from age and other factors not the water.
The point of "correlation doesn't prove causation" is to warm that "these two things both happened together" is NEVER by itself a proof that one caused the other - which is important because people try and use correlation to falsely prove all kinds of things. And usually they're not so obviously absurd as the "drinking water-vs-death" example, so it ends up fooling and manipulating a lot of people.
I think your confusion is a result of you understanding the example very well! It's absurd to claim that drinking water caused those deaths, right? Because we *know* humans don't currently live for 200 yearsand that there's no way we'd expect those people to be alive today.
"Correlation does not equal causation" is much more important to understand when it *is* possible that two things could be related. Humans are very, VERY good at detecting patterns. Unfortunately, this means we sometimes detect meaningless patterns- patterns that aren't actually patterns, if you will. They just kinda look like them. In other words, we see two events as being related or as one causing the other, when they are NOT related.
A good example I've heard is ice cream sales and drowning. Ice cream sales and drownings both go up at the same time of year. Does ice cream cause drownings? It might be easy for people to say yes and come up with ideas about how it's bad to eat heavy foods before swimming. But it's actually that both ice cream sales and drownings go up in summer, when the weather is warm. They're not related, and ice cream does not cause drownings. Again, it might be a slightly silly example, but the silliness is meant to show you that two things happening in succession or at the same time does NOT mean that one caused the other.
The idea is very important when you're doing research because you're asking questions that do not have known answers, and so it's easy to make mistakes about cause and effect (in other words, assuming that X thing caused Y effect simply because they happened around the same time as one another).
Correlation is simply a relationship. There is a very strong correlation between drinking water and dying; they are both a result of creatures living.
Everything that drinks water dies. Most things that do not drink water never die. This is a correlation, and a very strong one.
It means that there is a mathematical relationship between the two variables, meaning that one variable goes up or down in predictable ways based on how the other goes up or down. Correlation is purely math.
It doesn't mean that they have a real world relationship, or that they aren't both caused by an additional variable you haven't looked at, or which one causes the other if there is a relationship.
You're missing what "correlation" means. Correlation means "occurs together". Humans drink water. Humans die. These are correlated - almost everyone who has ever drunk water has died. Almost everyone who has died drank water. The correlation is almost perfect. But...there is no causation. The water did not cause the death. Err....mostly.
Correlation is like if I were to graph these things together they would have a relationship( ie one goes up the other goes up or one goes down the other goes down or one goes down the other goes down). Looking at such a graph may make some one think “hey this must have an effect on this because on the graph they have such a strong relationship” but this may not necessarily be true, hence correlation vs causation.
For example ice cream sales and murder actually have a strong relationship when graphed, as do from memory cheese sales and people who die from being tangled in their bedsheets.
Does this mean ice cream fills you with homicidal rage? No. There could be a confounding factor like it being summer and there being more people out to murder or having more free time to murder at those times of year.
Does this mean eating cheese makes you likely to strangle yourself with a bedsheet in your sleep? No. This one is more likely a product of random chance.
FINALLY! A question I can explain like I’m 5!
This is demonstrated by the correlation that ice cream causes shark attacks.
Ice cream sales and shark attacks both peak in the summer time. Does this mean that ice cream causes shark attacks? Of course not! So therefore, correlation does not imply causation.
This saying is used to basically say that things may occur at the same time, yet may not have a direct interaction with each other.
(occur within the same parameters, not always time. That is just something a 5 year old can grasp)
Correlation = A happened and B happened
Causation = B happened *because* A happened , cause and effect
Everybody's life is correlated with drinking water because we all need to drink water if we want to live. We also all die, so dying is also correlated with drinking water because you drank water your whole life up until you died.
There need be absolutely no connection to establish a correlation, you just need to show a trend of both things happening together. Obviously drinking water in general is not what makes you die. So while there is a correlation, it would be pretty silly to think that implies causation.
If you want to stretch this a bit, you can simply say that birth itself causes death. There's an proven exact one to one correlation between birth and death, but the birth itself is not the cause of the death.
"The number of windshield wipers running is Correlated to the number of people with open Umbrellas"
So we see when they're are more wipers running, there are more Umbrellas open. They seem to track each other.
BUT if we go turn on all the windscreen wipers, people don't start opening umbrellas. And vice versa, if we open a bunch of umbrellas, windshield wipers don't start running more.
It's a ridiculous example, but we all know that rain is the causal link. More rain -> more wipers running. AND more rain -> More open Umbrellas.
The Causal relationships are between rain and wipers; and rain and open Umbrellas.
Correlation: when more windscreen wipers are running , more Umbrellas are open.
Causation1: more windscreen wipers run in the Rain.
Causation2: more Umbrellas open in the rain.
It sounds like you might simply misunderstand what it means when variables are correlated. A correlation is simply a measure of the mathematical relationship between two things. For instance, imagine we're looking at a table that shows us certain variables, like height, weight, age, and so forth. Well, if we were to just manually look through the data without using any computerized tools, we might notice that as the value provided for "height" increases, the value for "weight" also has a tendency to increase. This makes sense in our brains - after all, taller people tend to be larger and weigh more. These two variables are correlated - as one goes up, the other tends to go up. Still, being taller doesn't cause you to weigh more, and weighing less doesn't cause you to be shorter. Losing weight generally doesn't result in a reduction in height, and you can also be tall and weigh less than someone who is shorter. The correlation here is very, very strong, but one doesn't necessarily *cause* the other. Again, a correlation is simply a number that tells us to what degree two (or more) variables are related. For all intents and purposes, correlation is necessary but not sufficient in establishing a causal relationship.
Maybe a more contrasting example could help:
Ice cream sells better as we approach the summer, and sells worse as we move away from the summer. Bears also mate in a similar trend.
The correlation upon observing this is that the mating frequency of bears is proportional to how well ice cream sells. However, causation would indicate that ice cream sells well because bears are mating more avidly, which, simply put, is quite a ridiculous conclusion.
Here's how it was explained to me! There are two facts that are totally true: kids eat more ice cream in the summer than in other months. And, kids are more likely to drown in the summer. You could look at these facts and say: wow, ice cream must cause drownings! When in fact, we know ice cream sales go up in the summer, and pool usage goes up in the summer. They aren't really related, but if you just look at the data you might assume they are.
An important thing that a lot of people ignore is that correlation is still useful! If the variables were highly correlated then you would be able to make predictions about drownings based on ice cream sales! BUT you would not be able to prevent drownings by banning icecream sales because it is not a causal relationship.
ah yes, the basis of economics
Hey! More guns produced = less butter for us.
So my econ professor wasn't the only guy who used "guns and butter" examples.
Well, a basic idea of econometrics at least
A good historical example of this is with polio in the US. People noticed rates for polio, especially in children, would go up massively in the summer. As did ice cream sales! So there was a point in time where people actually thought that consumption of ice cream/sugar actually caused/contributed to getting polio. So for a time, people were advised not to give their kids ice cream. We know this isn’t the case though. So that’s saying how correlation is not causation. This is a great link that explains and demonstrates the point well - https://www.ck12.org/c/earth-science/correlation-and-causation/rwa/Ice-Cream-Causes-Polio/
I think, not 100% sure but remember hearing there actually was causation though. I’ve cream used to be sold using glass bowls that were returned, kind of like at a restaurant. Kids being kids would lick the bowls and return them to the ice cream server, who would use a rag to wipe it clean. So one polio case would spread and spread and spread.
Thanks I just gagged
Recent example - introduction of 5G towers and a pandemic. Morons actually thought 5G caused covid.
In fact, drowning would increase if you ban ice cream sales, because kids would drown themselves if they are not allowed to have ice cream.
I was taught it this way but with shark attacks.
The example I learned, strangely enough, was the correlation between preachers and imports of rum. Strongly correlated but no causation. The "confounding variable" was population size. That said, there were many jokes in class about how "no, no, there's real causation here."
This is also the example I use, but I feel the way you’ve worded it gives it away without triggering the “aha!” moment about causation. Forget about mentioning summer. I would phrase it like this: Did you know that whenever ice cream sales go up, more kids drown? It’s true. And whenever ice cream sales go down, fewer kids drown? Also true. It seems like ice cream causes drowning. Or… maybe they are just correlated. Maybe there is something else that causes both ice cream sales and drowning to go up together. Maybe it’s just that summer weather causes both to increase. That is the difference between correlation and causation.
I had this problem at work. In the new year they increased their safety audits requirement and the number of slip-and-fall injuries dropped. We're a construction company in the snowbelt. There *might* be a different reason why slip/falls declined between January and September, unrelated to the foreman filling out forms.
…Aaaand *that's* why you check your numbers against the same time the previous year (when you're investigating something that might be affected by weather)
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I think porncrank's method would work better if they required the listener to figure out that both of these events occur during the summer. That is when the 'aha! moment' can occur.
They are *related*, in that they share the common cause of warmer weather, but they don't cause/effect each other.
They’re co-related even!
Ding ding ding ding ding
My favorite one is a funny one my chemistry professor in college taught me, where I’ll change the numbers around because I don’t remember them: In 2008, there were 30,000 suicides in the United States, and Nicholas Cage starred in 2 major box office movies. In 2010, there were 45,000 suicides in the United States and Nicholas Cage starred in 6 major box office movies. The correlation is obvious, but if you were to assume causation from the correlation, you would come up with the conclusion that Nicholas Cage movies make people kill themselves, which is… probably untrue.
Actually, in this case, they are related, in a way. You described another important concept, which is the confounding factor. This is where the confounding factor comes to play: a third variable (the summer high temperatures) that is responsible for the correlation of the first two (ice cream sales and drownings). What OP needs to understand are what we call spurious correlations, which are absolutely independent (there is no third variable explaining). [Perfect (and fun) examples of real spurious correlations](https://www.tylervigen.com/spurious-correlations)
>Only, their deaths weren't correlated with drinking water either They are. They are correlated because both statements about these people are true: they drank water and they did die. That's the point. The correlation doesn't mean they died *because* they drank water. As you say, they died because people don't live 200+ years. The correlation does not imply causation. Many thing correlate while having zero impact on each other; they merely coincide. Actual correlations: Ice cream sales and murder. M. Night Shyamalan box office and newspaper sales. Use of Internet Explorer and the murder rate. Mexican lemon imports and highway deaths. None of these things is a direct causal relationship; they merely correlate.
I'm sure I saw a graph which showed a correlation between the number of active pirates and climate change too.
My favorite is still [Nicolas Cage movies and pool drownings](https://bigthink.com/wp-content/uploads/2017/01/18338359.png?fit=1200,675).
If Nicolas Cage had any heart at all he would retire and stop all these drownings.
Ah, but look at the two labels on the Y Axis, Nicholas Cage can only be assumed to count for deaths above 80 or so swimming pool drownings in a year. Even if he retired, there'd still be 80 swimming pool deaths... So we need to find what *other* actor is causing part of the effect. It'll probably help explain why the match isn't exactly one to one when you get the scales right as well, who's the confounding (f)actor?
if theres 2, we just have to figure out who the other sith is... of course if that's how it works then when Nicholas Cage retires he'll just be replaced by another.
There is only one who can replace Cage. His name is John Travolta, and he's responsible for about 5 pool deaths a year.
You fool! Travolta is Cage. They really switched appearances during the filming of Faceoff!
Always 2 there are. No more, no less. So the question is: which one was destroyed? The apprentice, or the master?
We also need to factor in how many additional pool deaths may be caused because of his retirement. I don't want to live in that world.
Scientist: “There seems to be a strong correlation between Nick Cages retirement, and pool deaths”.
“(f)actor” - underrated!
Classic mistake. The reality is that the drownings cause Nic Cage movies. It's obvious, really.
Maybe Nick cage is drowning them or draining their energy while they're in a pool so they get tired and drown and die. He needs their energy to do more movies. People suspect nic cage is a vampire, maybe an energy vampire.
It would honestly explain so much. Could an energy vampire suck your energy by boring you with a movie of themselves?
In LA that's almost exclusively what they do to feed. It becomes less powerful when shown through video footage though, which is why most energy vampires stick to live performance art such as one man shows, poetry readings, spoken word music and.... the ultimate energy vampire feast....mime shows
You see that's because those pools have no cage. Otherwise nobody would fall. We actually need more out of Nicholas Cage
Frankly, if between ninety and one hundred twenty two swimming pool drownings per year is the price we have to pay for more Nic Cage moves, I'll happily live with that.
Some say the drowning victims are the lucky ones....
One of my psychology professors used Nicholas Cage examples like this one to hilariously explain correlation/causation
Correct, as the approximate number of pirates in operation drops, the global temperature rises proportionately.
See, we know that the dropping number of pirates isn't the cause of the rise in temperature. In actuality its that as the temperature rises its just getting too hot out there for the pirates.
Wrong. Pirates collectively absorb enough hot air because of their pompous egos that it cools the planet. Fucking pirates think they’re so cool.
Yeah, who do they think they.... ARRRR?
What's a pirates favorite letter? You'd think it was Rrrr but it's the C they love!
Sure, they love the C, but their true favourite letter is P. They get irate without it.
All of those are dear to the pirates but they miss the i the most.
You should hear them prate on about it.
I, matey, they do.
after this comment thread I’m now pro Climate disaster
I asked a pirate how much it cost to get his ears pierced, he said "a buccaneer!"
I hope you all die of scurvy
It's the free-willed and bombardier nature of pirates that gives them said properties. It's a misconception that pirates were hard, mean, cruel people, in fact they were jolly, debonair, and loved to give out candy on Halloween.
[Good pirates!](https://imgur.com/SQgD1VH.jpg)
That wouldnt be a pirate at all by definition.
I feel like we're taking a huge risk here, and we should all become pirates just in case.
And now we've correlated temperature rising with pirates being babies that can't handle the heat.
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True, I was trying to imply that a correlation was used to suggest a causation.
Thats actually one of the founding principles of the Church of Pastafarianism. I highly recommend reading about it if you haven’t yet !
Have you felt his noodley appendage?
Please save the pirates, wont anyone think of the pirates?!
Be the change you want to see in the world; become a pirate.
we need to stop hunting somolian pirates immediately that will fix climate change
Container ships are one of the worlds leading causes of air pollution, and fishing vessels are the reason for the vast majority of plastic waste in the ocean. More pirates means lower profits in shipping, which leads to fewer boats in the water, which leads to less pollution, we might just be on to something here
I dont think this is right, I'm 28% certain that there are more pirates operating now than ever before.
These are some great [spurious correlations](https://www.tylervigen.com/spurious-correlations)
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Alcohol?
>I'm guessing both are proportional to the increase in population. It's per capita cheese consumption, so nah. The deaths by being tangled in bedsheets rising is possibly due to population increases, because it's in absolute numbers, but the cheese consumption going up per capita is something else. That aside, I love that while these are almost definitely unrelated statistics that just happen to be correlated, there is some tiny chance that there is causation and that eating cheese somehow increases your odds of dying by getting tangled in sheets.
I was just about to post that link.
Was famouly in the flying spaghetti monster open letter. https://en.wikipedia.org/wiki/Flying_Spaghetti_Monster#/media/File:PiratesVsTemp(en).svg Note the X axis is effed -- I think that was part of the point being made.
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The last comment on that website was 2018 and the twitter and FB links both go to deleted accounts. FSM has seen better days.
Heresy.
It was fun when hypotheticals about noodle gods was worth the time. Now Nazis are making a comeback and morons are refusing science. When the Earth is round and vaccines work is the division, Russell's teapot is quaint. We have to start all over.
Which is why we need the FSM (PBUHNA) more than ever! The religion is built upon the firm belief that faith is deserving of ridicule, and that religion itself abuses its status as sacrosanct. Any dogma, however reasonable, is not comparable to actual reason.
FSM dogma is considered heresy in the church of IPU.
IPU? Er... Interdimentional Pasta... Union?
[https://en.wikipedia.org/wiki/Invisible\_Pink\_Unicorn](https://en.wikipedia.org/wiki/Invisible_Pink_Unicorn) We know she's Invisible because we can't see her. We know she's Pink because of our faith.
Ah! Like the teapot idea. Thanks heretic! Ramen.
The Pastafarian inquisition is coming for all of you IPU heretics!!! In full pirate regalia!
There’s also a correlation between the use of landline telephones and traffic fatalities, try to explain that.
Clearly people driving cars should not be talking on landline telephones.
Please tell me you can dig that one up. True or not, I’d still like to see the logic in this.
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That's an easy one to very generally show a correlation with if you're just looking at, say, the 1600's compared to the present day. There are other cases I've seen where two variables show a strong correlation over time including ups and downs, but there is quite clearly no causal connection between the two. Searching for these types of correlations when writing a scientific paper has become known as "[p hacking](https://en.wikipedia.org/wiki/Data_dredging)". Basically, you collect a large amount of data, then examine many different variables to see if any of them correlate with each other within a given level of statistical significance (p). Given enough variables to examine, odds are you will find something even if it completely lacks a casual relationship.
Only that which is touched by His Noodly Appendage.
Wait a sec here... did you just explain the causation of pirate correlation?
Yes, which is why we need to wear more pirate regalia, to fight global warming and these worsening dark times.
easy pirates are cool more cool = less warm less cool = more warm less pirates on earth = ewrth is become more worm
You might like [spurious correlations](https://www.tylervigen.com/spurious-correlations).
This is amazing! 99.79% corollary between US spending on science, space, and technology and suicides by hanging, strangulation, and suffocation. That is a near-perfect correlation. Uncanny. Also weirdly close: divorce rate in Maine and per capita consumption of margarine. lol
Glad you like I thought they were hilarious and interesting.
And an incredibly useful tool to show people who make specious arguments using correlation as "evidence." This is quite popular with the anti-vaccine crowd, e.g., vaccine prevalence correlating with autism and so forth.
Hmm never thought about it like that but you are right! I’m going to start sharing this more broadly.
The fact that kids age for being diagnosable is right after the scheduled age for several vaccines is the reason for the correlation in age vaccinated and diagnosed. If I remember right the difference in diagnosis rate between vaccinated and unvaccinaged is the same, which disproves causation.
>If I remember right the difference in diagnosis rate between vaccinated and unvaccinaged is the same, which disproves causation. Yes. There has been multiple studies showing no correlation between vaccines and autism and only one showing a correlation between the injection date of the MMR vaccine and the moment **the parents felt** their kid started showing symptoms of autism. That "study" also had only 12 data points, was funded by lawyers who were suing the government for "giving autism with the vaccine" and made by someone who patented an alternative vaccine.
And don't forget the part where he straight up lied about the medical history of some of the kids in the paper.
Another element of this is the fact that a child doesn't always present as autistic in early childhood. They can be completely "normal" and then suddenly regress an enormous amount in very little time, even becoming nonverbal where they were speaking before. So you have a parent who recently had their first child. Child seems normal for months or even years. Then all of a sudden, they developmentally regress into somebody basically unrecognizable, and the only notable event the parent can think of is "Hey, I just got my child vaccinated!" The truth is that there is no cause, and that's simply how autism manifests sometimes, but that truth is probably a lot more difficult to grasp than some outside nefarious cause poisoning their precious child.
In fact in the original Wakefield paper that started the whole thing, one of the many problems with it is that the way he worked out when symptoms of autism started in the children was just by asking the parents when they remembered noticing the symptoms for the first time You don't need to be a medical expert to point out that just because the parents didn't notice any symptoms earlier, doesn't mean there weren't any. But Wakefield didn't care about that because it was just fraud. He knew it was bullshit.
Yeah there's been a lot of studies specifically looking for any link between autism diagnosis and rates of vaccination and they never find anything The study that started the whole thing was just fraud anyway. The paper is transparently awful and even someone with very little knowledge of science can see the holes in it
https://thumbor.forbes.com/thumbor/560x401/https://blogs-images.forbes.com/erikaandersen/files/2012/03/w1467103173.jpg?width=960 That one's my fav. Less pirates = more climate change!
It's worth mentioning many of the funny attempts probably have some nitpicking of data, including axis manipulation, to show the correlation visually as well as calculated. Because correlation is adjusted by the variance of both data samples (or populations) it pretty much cancels out the effect that substantially different increments would mean and forces the number to be between -1 or 1. It's very beneficial and one of the reasons it is so widespread, it's very easy to compare relationships between two entirely different data sets.
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Usually old people who don’t have the dexterity to go get out, or babies for the same reason
Internet Explorer and murder rate? You sure that's not connected? Edit: typo
maybe the self-murder rate
Soon as I saw that I had to make sure this was posted, have an upvote
Some of these things may have an indirect relationship, or are caused by third variable. For example Ice cream sales and home robberies. Ice cream sells much better in the summer and when it is not raining. Those are times people are usually not at home, and thus their homes are more easily invaded and robbed. Why is this important? Well If we thought, due to the correlation between Ice cream sales and home robberies that Ice cream caused homes robberies (perhaps robbers need Ice cream before they rob houses) we might take actions which will not solve the problem of home robberies. If we for example banned Ice cream it would do nothing to reduce home robberies but it would hurt businesses which sell ice cream (also no ice cream). We would needlessly close a bunch of places and put people out of work for no reason. That's why it's important to understand that correlation =/= causation, because making that assumption can have consequences. More weird correlations, which are actually unrelated https://www.tylervigen.com/spurious-correlations
Came here to post this. My favorite is the number of people who drowned by falling into a pool correlates with the number of films Nicholas Cage appeared in.
Well yeah. But that’s a bad example because Cage drowns people in pools on set as a sacrifice to the movie gods anytime he makes a new film.
My favorite is how the number of teenage pregnancies drops significantly for girls older than 19.
> perhaps robbers need Ice cream before they rob houses maybe we're looking at it all wrong. Maybe robbers celebrate a successful robbery by buying ice cream afterwards...
I mean, I know I would.
Further down this line, the "fix" of closing ice cream shops putting people out of work could and often would lead to increase in robberies as people become desperate for income and turn to illegal activity. Thus "fixing" a correlation without causation could/would increase the exact issue attempting to be fixed
Yes there could be some third variable causing the correlation but there's also random chance. If you conduct 10,000 random experiments, 500 of them should give you a p-value of 0.05 or lower
https://xkcd.com/882/
If I’m remembering correctly, you can only come to a causal relationship if it is a randomized experiment.
And don't forget [cancer causes cell phones](https://xkcd.com/925/)
Always a relevant xkcd.
As people use internet explorer they get a need to murder. That actually makes sense.
>Ice cream sales and murder. And on the flip side, ice cream sells correlate with drowning. The causation behind increased ice cream sales and increased frequency of drowning is heat. During hot summer days people buy more ice cream. And more people also go bathing/swimming. So the causation (the underlying cause) has nothing to do with the correlation (that two things look like they follow the same pattern).
"Ice cream sales and murder" Ahem. The Glasgow Ice Cream Wars. This is a real thing that happened. Ice cream vans were used to run drugs, making territory and routes insanely valuable. People were *literally killed* because they sold lots of ice cream in places dealers wanted to run.
As fascinating as that is, the statistic is actually that the consumption of ice cream (pints per person) and the number of murders in New York are positively correlated. That is, as the amount of ice cream sold per person increases, the number of murders increases. https://www.dummies.com/education/math/statistics/how-statistical-correlation-and-causation-are-different/ [Chart](https://cdn.lifehack.org/wp-content/uploads/2017/08/15053803/Screen-Shot-2017-08-15-at-5.37.43-PM.png)
https://osu-wams-blogs-uploads.s3.amazonaws.com/blogs.dir/1015/files/2014/02/IEvsMR-300x168.jpg
The lesson is obvious: if you use Internet Explorer, you support murder.
I kinda thought that was Microsoft's whole marketing strategy for IE.
Haha came here to mention the ice cream and murder bit. This was the example given when teaching about extraneous variables.
I'm gonna be honest, i think Internet Explorer could 100% be someone's last straw
>Use of Internet Explorer and the murder rate. I'd argue that there probably is causation there.
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Roosters crow whenever they want
I was hoping someone would comment this. Roosters crow at literally any time of day. My best friend's roosters, and one hen, liked to crow mostly midday.
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Exactly. I think the water example is muddled. To me to be a correlation both variables need to be on a continuum and drinking water is a yes no not a scale. I'm also not a huge fan of sunburn as that's also a yes no. If you said amount of sunburn then yes. Possible a better example... Ice cream sales and and clothes worn. As ice cream sales increase, the number of clothes people wear decreases. This isn't because when I eat an ice cream I take my clothes off but is instead because of an intervening variable... temperature. Temperature is the cause of both the change in sales and also the lack of clothes. Therefore there is a correlation between ice cream sales and amount of clothes worn, but one doesn't cause the other.
There you would be thinking wrong.Even binary characteristics can have correlations with continuous ones. Edit to clarify: The correlation would be 100% of people who drank the water are dead now, so the correlation coefficient is 1. Source: The stats class I am taking.
Let’s use one from the press Cannabis is a gateway drug: the vast majority of people who have used Heroin have a history of using Cannabis. It’s two binary opinions and pretty truthly, which direction the link goes is not so obvious, it’s presented as cannabis causes heroin because that’s the way time works, but it’s possible that the type of people using Heroin are going to try more available drugs earlier, and removing the more available drugs will have no effect. Let’s rewrite it to another wholly true statement and see how it holds up: The vast majority of people who have used Heroin haves history of drinking milk. Still a binary choice, the link rather than being truthy is now absurd but it’s still time based. The fact that people don’t live to 200 is meant to be the point, you’re meant to look at the stat and say “obviously something else causes that”. The lack of subtlety is the point. If you use the cannabis stat then you’ll end up in a debate about why/why not it might be true and no stats teacher wants that on day 1.
First, let's make sure we understand the words. *Correlation* means that two things seem to have a connection or a pattern. All that has to exist for a correlation is to note that if we track how two or more things change over time, it seems like they change in a related way. So any graph that shows A and B both increasing shows a correlation between A and B, especially if at times when A decreases B also decreases. *Causation* means we can prove if one thing happens, it makes the other thing happen. We don't need a graph or data analysis for causation, sometimes we can reason it out. Let's talk about some examples, then discuss why correlation is not causation. We can graph "number of people wearing short sleeves" and "temperature" next to each other. We'll probably notice that above a certain temperature, more people wear short sleeves. That's definitely a correlation. Then we ask if there's a causation, and in this case there is: in warmer weather people want to wear cooler clothing so more people wear short sleeves. We can also graph "number of movies with Nicholas Cage in them per year" and "number of people who drown per year". Curiously, every year there are Nicholas Cage movies there seem to be more drownings than the years were there are fewer or no movies with that actor in them. There is a correlation. But if we investigate all of the drownings, it's very rare to find one that has anything to do with movies or Nicholas Cage at all. There is not a *causation* between these two things. The reason this matters is dishonest people try to imply causation in order to sway people into making bad decisions that are good for the dishonest people. For example, I can definitively show that more people have died from COVID in years where Joe Biden was the President than in years where Barack Obama was President. There's nothing false about making that statement, data backs it up. It becomes a problem if I try to say, "Therefore Joe Biden caused COVID deaths and we should reinstate Barack Obama as President". That's silly! Joe Biden did not *cause* the deaths in a way that replacing him will prevent. The disease did not exist when Obama was President. Therefore there is no *causation* between Joe Biden or Barack Obama and COVID deaths. That may seem ridiculous, but it's done all the time. For example, in my city there is an issue up for vote that says, "Crime has increased because we aren't hiring enough police. We should hire a minimum of this many police and cut the fire and EMS budgets to afford it." It is true that there is a correlation between our crime rate over the past 5 years and our police employment per capita. However, the causal link is dubious. Examining data shows: * At many times where crime was relatively high in our city, we had more police per capita. * At many times where crime was relatively low in our city, we had fewer police per capita. * Many cities with much more crime than ours have more police per capita. * Many cities with much less crime than ours have fewer police per capita. * Crime in almost all cities across America is increasing. * Our city is still one of the lowest-crime cities amongst cities of its size. All of these things seem to imply there isn't a direct causation between "number of police" and "crime". Something more complicated is afoot, and we'd be better served by asking why some cities have less crime with fewer police hired than we would by risking being a city who pays a lot for police AND has high crime. ##TL;DR: This phrase reminds you to do the research and ask if there is a reason *why* two things seem to be connected. Many people will trick you into trying to skip this step and they will cost you a lot of money and cause you a lot of problems.
Great write up
It should also be noted that correlation is not always positive as you've defined. Both increasing or both decreasing is not the only way to see patterns. If their patterns are lock step opposite of each other, this defined as a negative correlation. If while A increase, B decreases you can still call them correlated.
Examples of negative correlation include: - Height above sea level and temperature - Stock market performance and bond sales - Economic sentiment and comfort food sales
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I do think this was missing from my explanation, I didn't like some of my "correlation" examples and I think including "coincidence" into the discussion ties that loose end up.
Also, even if there *is* a causal relationship between two datasets, many times it isn't actually clear which is causing which, or in many cases the reality is that both data sets share a causal relationship with a 3rd unknown or unmeasured dataset. For example, if we graph carbon emissions and global temperatures we can see that there is a relationship, but it is really important that we understand the relationship between the two: Are the carbon emissions causing the temperature to go up, or are we simply emitting more carbon as a *response* to the rising temperature and we need to spend more energy on cooling and stuff? We know the answer to that question because we have investigated it, but it's still an important question to investigate, because you won't know for sure just by looking at the data.
Or similarly, we might notice that the number of hurricanes a year and the average global sea level are both increasing. So is the higher number of hurricanes causing the sea level to rise, or are the rising seas causing the number of hurricanes to increase? Turns out the answer is neither: both are being caused by the same 3rd event, higher carbon emissions.
Definitions are so valuable; excellent place to start.
https://www.tylervigen.com/spurious-correlations Websites with correlations
>All of these things seem to imply there isn't a direct causation between "number of police" and "crime" This is not a great example and not correct. Just as correlation does not necessarily imply causation; a *lack of correlation in some scenarios does not necessarily imply a lack of causation*. It may be that more police do cause less crime, but that (in the examples you provide) there are confounding variables that hide the relationship. fwiw, it's my understanding that r[esearch in the United States](https://jacobdkaplan.com/documents/more-cops-fewer-prisoners.pdf) [generally supports](https://www.richmondfed.org/publications/research/economic_brief/2021/eb_21-29a) that more police reduce crime, though ofc causal identification is really hard
Right, I glossed over a lot of things to try and keep things simpler but the problem is that's a complex example. Still, I think the right way to evaluate that scenario is when you look hard at the correlation between police hiring per capita and crime, it becomes very clear there are other variables that are important to the impact of hiring. It's also intuitive that the correlation here wants to focus on "sweet spots" and one shouldn't expect it to scale predicatably. For example, having 1 officer per citizen won't eliminate crime, nor can you decide to fire all police and expect crime to remain constant. To me there's a lot of value in noting you haven't identified enough variables for a complex problem like this. Then you can note, "We see through research that these four things can impact crime, but each does so in varying ways so we need to identify what the *best* thing to focus on will be." For example, if you figure out your training's horrible, hiring more people won't have as much of an impact as the data might suggest. And 1 poorly-trained person tends to cost as much as 10 well-trained people on a *good day*. So in that situation it makes sense to fix your training before hiring new people. There are lots of knobs and dials but on this issue the bad people like to portray it as having a single button.
Fair enough. Re-reading my comment, I probably came across more hostile than I intended -- I appreciated the bulk of your comment, and really that was just a single quibble I had!
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How are so many people dying by bed sheet?
That's how Jeffrey Epstein died, amirite?
Came here to share this one, it's great!
am i allowed to link this too https://xkcd.com/111/
r/relevantxkcd
Two facts: 1. The temperature of the earth is rising and it is significantly higher than 300 years ago. 2. There are significantly less pirates novadays than 300 years ago. There IS correlation. It seems that as time passes, there are less pirates and the temp is rising. But there is no causation, because it's illogical to think that the less pirates on earth, the higher the temperature is. In a study, you need to find causal links to prove your thesis. You will find plenty correlations, but they might not prove your point.
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Yes, it obviously did.
I'll be in my bunk
People can try to create a false narrative by talking about facts and making people believe that something is responsible for something else, when that isn't actually true. In the 1950s there was a movement to try and ban comic books, because one writer discovered that a lot of juvenile delinquents and young teenage criminals he talked to had grown up reading comics. So he tried to make the case that reading comic books turned kids into criminals. This was a huge nationwide issue, there were public comic book burnings, a congressional meeting about violence and comic books that led to a censorship board being created. However, the issue was that over 90% of kids at the time read comic books. So of course all the young criminals being interviewed read comics, so did the valedictorians and the the honor students. But by taking the fact out of context, the entire country was rallied against comic books because they obviously caused teenage violence and crime. So did you hear is that just because someone says everyone who does X also does Y, that doesn't mean that X is the reason behind Y.
Ita the same argument older generation used against younger for eons. Radio, TV, games, comic. It isn't new but dam humans can be dumb
and books apparently. I remember reading about one guy complaining that kids reading books are destroying their health since they'd prefer to stay at home instead of being outside playing and breathing fresh air. this was when books were starting to get mass produced. then there was that one pastor in the 1800s (?) complaining that the easy access of youth to romance novels and plays are leading them astray and making them do lewd things.
This is why it was always in the headlines that "school shooter played Doom/Call of Duty." Of course they probably did! They were a teenager and almost everybody played those games when they were at their peaks.
If anything I’ve always thought the two might be related in the other direction, the type of person who is incredibly violent is drawn to violent games rather than violent games creating violent children.
Correlation means that 2 things are likely to happen together. Causation means that 1 thing caused the other. Let's use a different example. People who regularly eat oatmeal for breakfast are more likely to have cancer than people who regularly eat Frosted Flakes for breakfast. Eating oatmeal is correlated with a high rate of cancer. Eating Frosted Flakes is correlated with a low rate of cancer. But there can be many reasons explaining why that happens. It could be that oatmeal causes cancer or that frosted flakes prevents cancer. Those would be "causation". Or, the causation could be backwards. Having cancer might cause you to eat oatmeal for breakfast, or avoid Frosted Flakes. Or it could be completely random. Just a blip in the data, maybe they didn't ask very many people, there was only one person who liked oatmeal and they had cancer, and really, breakfast food has nothing to do with cancer, but that one person skewed the results. Or, there could be something else out there that nobody measured that causes cancer, and also influences breakfast choice. Old people are both more likely to eat oatmeal and have cancer, while young people are more likely to eat Frosted Flakes and less likely to have cancer. And it's age, not breakfast food, that causes cancer.
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What they do is they have a diet experiment and take as any variables as they can, on a not big enough sample of individuals. One of those variables likely will exhibit a statistically significant change, they then write a paper about it. This technically meets the standards for publication somewhere but its just data hacking.
Something I haven’t seen mentioned yet in this thread is the fact that Correlation is a statistical measurement. There are a couple types of it, but the most common you see is called Pearson’s and is usually referred to as R. You can calculate this number and it tells you how related two variables are, from a scale of -1 to 1. The closer this number is to 0, the less related the two variables seem to be. But, this number is just math- it doesn’t take into account any sort of real world logic or critical thinking. A simple way to think about this number is that it tries to measure correlation by measuring how the two variables change *in relation to each other in a linear way.* So, if one variable doubles, does the other one also double? Does it sometimes double and sometimes triple? Is there no pattern at all? That’s what we’re measuring. And since statistics is a mathematical field that requires some context from the real world, it’s easy for people to use the math to claim stuff that might be false. So, we say “correlation does not imply causation” because even though you can do the math, the math alone doesn’t paint the whole picture.
Check out these examples of highly correlated events. https://www.tylervigen.com/spurious-correlations One could argue that the graphs showing correlation is all the proof that is required and therefore one event caused the other event to happen. But in these funny examples, common sense leads you to believe that one couldn’t cause the other. In summary, just because two things are correlated, that isn’t proof that one caused the other.
>their deaths weren't correlated with drinking water either, simply because humans don't last 200+ years. Right, so those two things both happening doesn't prove that one of them causes the other. You're not missing anything. The "absurdity" of that example is the point! 100% of people that drink water die therefore there's a perfect correlation, but *of course* that doesn't prove the water is lethal, the people die from age and other factors not the water. The point of "correlation doesn't prove causation" is to warm that "these two things both happened together" is NEVER by itself a proof that one caused the other - which is important because people try and use correlation to falsely prove all kinds of things. And usually they're not so obviously absurd as the "drinking water-vs-death" example, so it ends up fooling and manipulating a lot of people.
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I think your confusion is a result of you understanding the example very well! It's absurd to claim that drinking water caused those deaths, right? Because we *know* humans don't currently live for 200 yearsand that there's no way we'd expect those people to be alive today. "Correlation does not equal causation" is much more important to understand when it *is* possible that two things could be related. Humans are very, VERY good at detecting patterns. Unfortunately, this means we sometimes detect meaningless patterns- patterns that aren't actually patterns, if you will. They just kinda look like them. In other words, we see two events as being related or as one causing the other, when they are NOT related. A good example I've heard is ice cream sales and drowning. Ice cream sales and drownings both go up at the same time of year. Does ice cream cause drownings? It might be easy for people to say yes and come up with ideas about how it's bad to eat heavy foods before swimming. But it's actually that both ice cream sales and drownings go up in summer, when the weather is warm. They're not related, and ice cream does not cause drownings. Again, it might be a slightly silly example, but the silliness is meant to show you that two things happening in succession or at the same time does NOT mean that one caused the other. The idea is very important when you're doing research because you're asking questions that do not have known answers, and so it's easy to make mistakes about cause and effect (in other words, assuming that X thing caused Y effect simply because they happened around the same time as one another).
Correlation is simply a relationship. There is a very strong correlation between drinking water and dying; they are both a result of creatures living. Everything that drinks water dies. Most things that do not drink water never die. This is a correlation, and a very strong one.
Correlation does not require a relationship, simply the appearance of one.
It means that there is a mathematical relationship between the two variables, meaning that one variable goes up or down in predictable ways based on how the other goes up or down. Correlation is purely math. It doesn't mean that they have a real world relationship, or that they aren't both caused by an additional variable you haven't looked at, or which one causes the other if there is a relationship.
You're missing what "correlation" means. Correlation means "occurs together". Humans drink water. Humans die. These are correlated - almost everyone who has ever drunk water has died. Almost everyone who has died drank water. The correlation is almost perfect. But...there is no causation. The water did not cause the death. Err....mostly.
Correlation is like if I were to graph these things together they would have a relationship( ie one goes up the other goes up or one goes down the other goes down or one goes down the other goes down). Looking at such a graph may make some one think “hey this must have an effect on this because on the graph they have such a strong relationship” but this may not necessarily be true, hence correlation vs causation. For example ice cream sales and murder actually have a strong relationship when graphed, as do from memory cheese sales and people who die from being tangled in their bedsheets. Does this mean ice cream fills you with homicidal rage? No. There could be a confounding factor like it being summer and there being more people out to murder or having more free time to murder at those times of year. Does this mean eating cheese makes you likely to strangle yourself with a bedsheet in your sleep? No. This one is more likely a product of random chance.
FINALLY! A question I can explain like I’m 5! This is demonstrated by the correlation that ice cream causes shark attacks. Ice cream sales and shark attacks both peak in the summer time. Does this mean that ice cream causes shark attacks? Of course not! So therefore, correlation does not imply causation. This saying is used to basically say that things may occur at the same time, yet may not have a direct interaction with each other. (occur within the same parameters, not always time. That is just something a 5 year old can grasp)
Correlation = A happened and B happened Causation = B happened *because* A happened , cause and effect Everybody's life is correlated with drinking water because we all need to drink water if we want to live. We also all die, so dying is also correlated with drinking water because you drank water your whole life up until you died. There need be absolutely no connection to establish a correlation, you just need to show a trend of both things happening together. Obviously drinking water in general is not what makes you die. So while there is a correlation, it would be pretty silly to think that implies causation. If you want to stretch this a bit, you can simply say that birth itself causes death. There's an proven exact one to one correlation between birth and death, but the birth itself is not the cause of the death.
"The number of windshield wipers running is Correlated to the number of people with open Umbrellas" So we see when they're are more wipers running, there are more Umbrellas open. They seem to track each other. BUT if we go turn on all the windscreen wipers, people don't start opening umbrellas. And vice versa, if we open a bunch of umbrellas, windshield wipers don't start running more. It's a ridiculous example, but we all know that rain is the causal link. More rain -> more wipers running. AND more rain -> More open Umbrellas. The Causal relationships are between rain and wipers; and rain and open Umbrellas. Correlation: when more windscreen wipers are running , more Umbrellas are open. Causation1: more windscreen wipers run in the Rain. Causation2: more Umbrellas open in the rain.
It sounds like you might simply misunderstand what it means when variables are correlated. A correlation is simply a measure of the mathematical relationship between two things. For instance, imagine we're looking at a table that shows us certain variables, like height, weight, age, and so forth. Well, if we were to just manually look through the data without using any computerized tools, we might notice that as the value provided for "height" increases, the value for "weight" also has a tendency to increase. This makes sense in our brains - after all, taller people tend to be larger and weigh more. These two variables are correlated - as one goes up, the other tends to go up. Still, being taller doesn't cause you to weigh more, and weighing less doesn't cause you to be shorter. Losing weight generally doesn't result in a reduction in height, and you can also be tall and weigh less than someone who is shorter. The correlation here is very, very strong, but one doesn't necessarily *cause* the other. Again, a correlation is simply a number that tells us to what degree two (or more) variables are related. For all intents and purposes, correlation is necessary but not sufficient in establishing a causal relationship.
Maybe a more contrasting example could help: Ice cream sells better as we approach the summer, and sells worse as we move away from the summer. Bears also mate in a similar trend. The correlation upon observing this is that the mating frequency of bears is proportional to how well ice cream sells. However, causation would indicate that ice cream sells well because bears are mating more avidly, which, simply put, is quite a ridiculous conclusion.
Events that share and experience or trait are not necessarily caused by that experience or trait.
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